Search Results - (((((((kant OR want) OR mantis) OR when) OR cantor) OR anne) OR shared) OR hints) algorithms.

Leadership and cultural webs in organisations : Weavers' tales by McLean, A. J. (Adrian J.)

Table of Contents: “…Organisation as Algorithm.…”
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Haptic Feedback Teleoperation of Optical Tweezers by Ni, Zhenjiang

Table of Contents: “…Specific designs for haptic interactions; 1.4.1. Temporal sharing; 1.4.2. Spatial sharing; 1.5. Discussion; 1.6. …”
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Field guide to the normal newborn by Emmett, Gary A.

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Machine Learning with Spark - Second Edition. by Dua, Rajdeep

Table of Contents: “…Generating predictions for the Kaggle/StumbleUpon evergreen classification dataset -- Evaluating the performance of classification models -- Accuracy and prediction error -- Precision and recall -- ROC curve and AUC -- Improving model performance and tuning parameters -- Feature standardization -- Additional features -- Using the correct form of data -- Tuning model parameters -- Linear models -- Iterations -- Step size -- Regularization -- Decision trees -- Tuning tree depth and impurity -- The naive Bayes model -- Cross-validation -- Summary -- Chapter 7: Building a Regression Model with Spark -- Types of regression models -- Least squares regression -- Decision trees for regression -- Evaluating the performance of regression models -- Mean Squared Error and Root Mean Squared Error -- Mean Absolute Error -- Root Mean Squared Log Error -- The R-squared coefficient -- Extracting the right features from your data -- Extracting features from the bike sharing dataset -- Training and using regression models -- BikeSharingExecutor -- Training a regression model on the bike sharing dataset -- Linear regression -- Generalized linear regression -- Decision tree regression -- Ensembles of trees -- Random forest regression -- Gradient boosted tree regression -- Improving model performance and tuning parameters -- Transforming the target variable -- Impact of training on log-transformed targets -- Tuning model parameters -- Creating training and testing sets to evaluate parameters -- Splitting data for Decision tree -- The impact of parameter settings for linear models -- Iterations -- Step size -- L2 regularization -- L1 regularization -- Intercept -- The impact of parameter settings for the decision tree -- Tree depth -- Maximum bins -- The impact of parameter settings for the Gradient Boosted Trees -- Iterations -- MaxBins -- Summary.…”
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World's greatest architect : making, meaning, and network culture by Mitchell, William J. (William John), 1944-2010

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Resources utilization and productivity enhancement case studies by Mital, Anil, Pennathur, Arunkumar

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Applied Missing Data Analysis in the Health Sciences. by Zhou, Xiao-Hua

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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…3.3 Constrained, Unconstrained, and Discrete Optimization3.3.1 Constrained Optimization; 3.3.2 Unconstrained Optimization; 3.3.3 Discrete Optimization; 3.4 Surrogate Modeling; 3.4.1 Overview; 3.4.2 Optimization Procedure; 3.4.3 Surrogate Modeling Approach; 3.4.3.1 Response Surface Approximation (RSA) Model; 3.4.3.2 Artificial Neural Network (ANN) Model; 3.4.3.3 Kriging Model (KRG) Model; 3.4.3.4 PRESS-Based-Averaging (PBA) Model; 3.4.3.5 Simple Average (SA) Model; 3.5 Error Estimation; 3.5.1 General Errors When Simulating and Optimizing a Turbomachinery System…”
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AI and human thought and emotion by Freed, Sam (Philosophy professor)

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Mobile magic : the saatchi and saatchi guide to mobile marketing and design by Eslinger, Tom

Table of Contents: “…. ; Surprise and Delight; A Short Radius Goes a Long Way; Cumulative Location-Tracking; The Creep Factor: When Location Goes Too Far; Surprise and Delight vs. …”
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Decision Intelligence for Dummies by Baker, Pamela

Table of Contents: “…Any road will take you there -- The great rethink when it comes to making decisions at scale -- Applying the Upside-Down V: The Path to the Output and Back Again -- Evaluating Your Inverted V Revelations -- Having Your Inverted V Lightbulb Moment -- Recognizing Why Things Go Wrong -- Aiming for too broad an outcome -- Mimicking data outcomes -- Failing to consider other decision sciences -- Mistaking gut instincts for decision science -- Failing to change the culture -- Part 2 Reaching the Best Possible Decision -- Chapter 5 Shaping a Decision into a Query -- Defining Smart versus Intelligent -- Discovering That Business Intelligence Is Not Decision Intelligence -- Discovering the Value of Context and Nuance -- Defining the Action You Seek -- Setting Up the Decision -- Chapter 6 Mapping a Path Forward -- Putting Data Last -- Recognizing when you can (and should) skip the data entirely -- Leaning on CRISP-DM -- Using the result you seek to identify the data you need -- Digital decisioning and decision intelligence -- Don't store all your data - know when to throw it out -- Adding More Humans to the Equation -- The shift in thinking at the business line level -- How decision intelligence puts executives and ordinary humans back in charge -- Limiting Actions to What Your Company Will Actually Do -- Looking at budgets versus the company will -- Setting company culture against company resources -- Using long-term decisioning to craft short-term returns -- Chapter 7 Your DI Toolbox -- Decision Intelligence Is a Rethink, Not a Data Science Redo -- Taking Stock of What You Already Have -- The tool overview -- Working with BI apps -- Accessing cloud tools -- Taking inventory and finding the gaps -- Adding Other Tools to the Mix -- Decision modeling software -- Business rule management systems -- Machine learning and model stores -- Data platforms.…”
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Advances in Materials Processing Technologies, 2006. by Marcos, M.

Table of Contents: “…Advances in Materials Processing Technologies, 2006; Preface; Table of Contents; Advances in Material Processing Technologies; A Heuristic Approach for Decision-Making on Assembly Systems for Mass Customization ; A New Method for Determining the Chip Geometry in Milling; Analysis and Validation of Cutting Forces Prediction Models in Micromachining; Analysis of Stress and Strain in the Equal Channel Angular Drawing Process; Analysis of the Behaviour Effect of Face Cutting Edge Inserts on Surface Roughness when Milling Steels with MQL Lubrication.…”
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Cinder Creative Coding Cookbook. by Dawid Gorny, Rui Madeira

Table of Contents: “…Creating a simple video controllerSaving window content as an image; Saving window animations as video; Saving window content as a vector graphics image; Saving high resolution images with the tile renderer; Sharing graphics between applications; Building Particle Systems; Introduction; Creating a particle system in 2D; Applying repulsion and attraction forces; Simulating particles flying in the wind; Simulating flocking behavior; Making our particles sound reactive; Aligning particles to a processed image; Aligning particles to the mesh surface; Creating springs.…”
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Frontiers of Artificial Intelligence in Medical Imaging. by Razmjooy, Navid

Table of Contents: “…5.5 Electromagnetic field optimization algorithm -- 5.6 Developed electromagnetic field optimization algorithm -- 5.7 Simulation results -- 5.7.1 Image acquisition -- 5.7.2 Pre-processing stage -- 5.7.3 Processing stage -- 5.7.4 Classification -- 5.8 Final evaluation -- 5.9 Conclusions -- References -- Chapter 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation -- 6.1 Introduction -- 6.2 Context -- 6.3 Methodology -- 6.3.1 COVID-19 database -- 6.3.2 Image conversion and pre-processing -- 6.3.3 Image thresholding…”
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Discrete element model and simulation of continuous materials behavior set. Volume 1, Discrete element method to model 3D continuous materials by Jebahi, Mohamed

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Twelfth Scandinavian Conference on Artificial Intelligence : SCAI 2013

Table of Contents: “…Trap Escape for Local Search by Backtracking and Conflict ReverseIncorporating Prior Knowledge when Learning Mixtures of Truncated Basis Functions from Data; Expansion of the Variational Garrote to a Multiple Measurement Vectors Model; Classification of Movement Patterns in Skiing; Combining Constraint Types from Public Data in Aerial Image Segmentation; Formalizing Theatrical Performances Using Multi-Agent Organizations; A Hierarchical Model for Continuous Gesture Recognition Using Kinect; Expressive Planning Through Constraints…”
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Proceedings of the 2009 International Conference on Computer and Network Technology, Chennai, India, 24-26 July 2009

Table of Contents: “…An Ownership Sharing Protocol for RFID without a Trusted Third Party G. …”
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Who's #1? : the science of rating and ranking by Langville, Amy N., Meyer, C. D. (Carl Dean)

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Prostate cancer : diagnosis and clinical management

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Applied mathematics for the analysis of biomedical data : models, methods, and MATLAB by Costa, Peter J.

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